The first attempt in generating vector images is by [1]} to generate Kanji (Chinese alphabet) characters using a two-layered LSTM, where each Kanji character is represented in the stroke-based format. Following [1]}, D. Ha et al. propose a VAE model called the “sketch-rnn", for vector sketch generation in [3]}, which is trained on the “QuickDraw" dataset [4]}. Here too, the sketches are represented in the stroke-based format. This paper shows very good performance in unconditional generation, conditional reconstruction, latent space representation and predicting the ending of incomplete sketches for a variety of classes of objects. [3]} produced visually appealing sketches when trained with a single category of sketch. The sketches are not visually appealing when a mix of category is used for training. Hence, in order to overcome this difficulty, [6]} replaces the encoder of [3]} with a Convolution Neural Network(CNN) and removes the KL-Divergence loss. This model too produces sketches in the stroke-based format. Since the convolution is spatial, the input to the this model is rasterized format of sketches from the QuickDraw dataset. Based on the Turing Test, the authors of [6]} conclude that the models with CNN encoders outperformed those with RNN encoders in generating human-style sketches. K. Zhong in [9]} extends the VAE proposed in [3]} to create an end-to-end pipeline which takes in fonts in Scalable Vector Graphics (SVGs) to learn and generate novel fonts. The results are demonstrated on Google Fonts Dataset.
